An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B

Investigating the Promising Anticancer Activity of Cetuximab and Fenbendazole Combination as Dual CBS and VEGFR-2 Inhibitors and Endowed with Apoptotic Potential

In this work, the cytotoxicity of monoclonal antibody (Cetuximab, Ce) and Fenbendazole (Fen), as well as their combination therapy were tested with the MTT assay. On the other side, Ce, Fen, and a combination between them were subjected to a colchicine-tubulin binding test, which was conducted and compared to Colchicine as a reference standard. Besides, Ce, Fen, and the combination of them were tested against the VEGFR-2 target receptor, compared to Sorafenib as the standard medication. Moreover, the qRT-PCR technique was used to investigate the levels of apoptotic genes (p53 and Bax) and anti-apoptotic gene (Bcl-2) as well. Also, the effect of Ce, Fen, and the combination of them on the level of ROS was studied. Furthermore, the cell cycle analysis and Annexin V apoptosis assay were carried out for Ce, Fen, and a combination of them. In addition, the molecular docking studies were used to describe the molecular levels of interactions for both (Fen and colchicine) or (Fen and sorafenib) within the binding pockets of the colchicine binding site (CBS) and vascular endothelial growth factor-2 receptor (VEGFR-2), respectively.

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model

Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network-Long Short-Term Memory (RCNN-LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN-LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1-score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes. RESEARCH HIGHLIGHTS: Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.

A Liver Stiffness-Based Etiology-Independent Machine Learning Algorithm to Predict Hepatocellular Carcinoma

BACKGROUND & AIMS

The existing hepatocellular carcinoma (HCC) risk scores have modest accuracy, and most are specific to chronic hepatitis B infection. In this study, we developed and validated a liver stiffness-based machine learning algorithm (ML) for prediction and risk stratification of HCC in various chronic liver diseases (CLDs).

METHODS

MLs were trained for prediction of HCC in 5155 adult patients with various CLDs in Korea and further tested in 2 prospective cohorts from Hong Kong (HK) (N = 2732) and Europe (N = 2384). Model performance was assessed according to Harrell's C-index and time-dependent receiver operating characteristic (ROC) curve.

RESULTS

We developed the SMART-HCC score, a liver stiffness-based ML HCC risk score, with liver stiffness measurement ranked as the most important among 9 clinical features. The Harrell's C-index of the SMART-HCC score in HK and Europe validation cohorts were 0.89 (95% confidence interval, 0.85-0.92) and 0.91 (95% confidence interval, 0.87-0.95), respectively. The area under ROC curves of the SMART-HCC score for HCC in 5 years was ≥0.89 in both validation cohorts. The performance of SMART-HCC score was significantly better than existing HCC risk scores including aMAP score, Toronto HCC risk index, and 7 hepatitis B-related risk scores. Using dual cutoffs of 0.043 and 0.080, the annual HCC incidence was 0.09%-0.11% for low-risk group and 2.54%-4.64% for high-risk group in the HK and Europe validation cohorts.

CONCLUSIONS

The SMART-HCC score is a useful machine learning-based tool for clinicians to stratify HCC risk in patients with CLDs.

Quantitative methods for optimizing patient outcomes in liver transplantation

Liver transplantation (LT) is a lifesaving yet complex intervention with considerable challenges impacting graft and patient outcomes. Despite best practices, 5-year graft survival is only 70%. Sophisticated quantitative techniques offer potential solutions by assimilating multifaceted data into insights exceeding human cognition. Optimizing donor-recipient matching and graft allocation presents additional intricacies, involving the integration of clinical and laboratory data to select the ideal donor and recipient pair. Allocation must balance physiological variables with geographical and logistical constraints and timing. Quantitative methods can integrate these complex factors to optimize graft utilization. Such methods can also aid in personalizing treatment regimens, drawing on both pretransplant and posttransplant data, possibly using continuous immunological monitoring to enable early detection of graft injury or infected states. Advanced analytics is thus poised to transform management in LT, maximizing graft and patient survival. In this review, we describe quantitative methods applied to organ transplantation, with a focus on LT. These include quantitative methods for (1) utilizing and allocating donor organs equitably and optimally, (2) improving surgical planning through preoperative imaging, (3) monitoring graft and immune status, (4) determining immunosuppressant doses, and (5) establishing and maintaining the health of graft and patient after LT.

EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC

We aimed to develop and validate a deep learning-based system using pre-therapy computed tomography (CT) images to detect epidermal growth factor receptor (EGFR)-mutant status in patients with non-small cell lung cancer (NSCLC) and predict the prognosis of advanced-stage patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors (TKI). This retrospective, multicenter study included 485 patients with NSCLC from four hospitals. Of them, 339 patients from three centers were included in the training dataset to develop an EfficientNetV2-L-based model (EME) for predicting EGFR-mutant status, and the remaining patients were assigned to an independent test dataset. EME semantic features were extracted to construct an EME-prognostic model to stratify the prognosis of EGFR-mutant NSCLC patients receiving EGFR-TKI. A comparison of EME and radiomics was conducted. Additionally, we included patients from The Cancer Genome Atlas lung adenocarcinoma dataset with both CT images and RNA sequencing data to explore the biological associations between EME score and EGFR-related biological processes. EME obtained an area under the curve (AUC) of 0.907 (95% CI 0.840-0.926) on the test dataset, superior to the radiomics model (P = 0.007). The EME and radiomics fusion model showed better (AUC, 0.941) but not significantly increased performance (P = 0.895) compared with EME. In prognostic stratification, the EME-prognostic model achieved the best performance (C-index, 0.711). Moreover, the EME-prognostic score showed strong associations with biological pathways related to EGFR expression and EGFR-TKI efficacy. EME demonstrated a non-invasive and biologically interpretable approach to predict EGFR status, stratify survival prognosis, and correlate biological pathways in patients with NSCLC.

Development and validation of artificial intelligence models to predict urinary tract infections and secondary bloodstream infections in adult patients

BACKGROUND

Traditional culture methods are time-consuming, making it difficult to utilize the results in the early stage of urinary tract infection (UTI) management, and automated urinalyses alone show insufficient performance for diagnosing UTIs. Several models have been proposed to predict urine culture positivity based on urinalysis. However, most of them have not been externally validated or consisted solely of urinalysis data obtained using one specific commercial analyzer.

METHODS

A total of 259,187 patients were enrolled to develop artificial intelligence (AI) models. AI models were developed and validated for the diagnosis of UTI and urinary tract related-bloodstream infection (UT-BSI). The predictive performance of conventional urinalysis and AI algorithms were assessed by the areas under the receiver operating characteristic curve (AUROC). We also visualized feature importance rankings as Shapley additive explanation bar plots.

RESULTS

In the two cohorts, the positive rates of urine culture tests were 25.2% and 30.4%, and the proportions of cases classified as UT-BSI were 1.8% and 1.6%. As a result of predicting UTI from the automated urinalysis, the AUROC were 0.745 (0.743-0.746) and 0.740 (0.737-0.743), and most AI algorithms presented excellent discriminant performance (AUROC > 0.9). In the external validation dataset, the XGBoost model achieved the best values in predicting both UTI (AUROC 0.967 [0.966-0.968]) and UT-BSI (AUROC 0.955 [0.951-0.959]). A reduced model using ten parameters was also derived.

CONCLUSIONS

We found that AI models can improve the early prediction of urine culture positivity and UT-BSI by combining automated urinalysis with other clinical information. Clinical utilization of the model can reduce the risk of delayed antimicrobial therapy in patients with nonspecific symptoms of UTI and classify patients with UT-BSI who require further treatment and close monitoring.

Hepatocellular Carcinoma Risk Scores from Modeling to Real Clinical Practice in Areas Highly Endemic for Hepatitis B Infection

Hepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers and represents a global health challenge. Liver cancer ranks third in cancer-related mortality with 830,000 deaths and sixth in incidence with 906,000 new cases annually worldwide. HCC most commonly occurs in patients with underlying liver disease, especially chronic hepatitis B virus (HBV) infection in highly endemic areas. Predicting HCC risk based on scoring models for patients with chronic liver disease is a simple, effective strategy for identifying and stratifying patients to improve the early diagnosis rate and prognosis of HCC. We examined 23 HCC risk scores published worldwide in CHB patients with (n=10) or without (n=13) antiviral treatment. We also described the characteristics of the risk score's predictive performance and application status. In the future, higher predictive accuracy could be achieved by combining novel technologies and machine learning algorithms to develop and update HCC risk score models and integrated early warning and diagnosis systems for HCC in hospitals and communities.

Prediction model of hepatitis B virus-related hepatocellular carcinoma in patients receiving antiviral therapy

Chronic hepatitis B virus (HBV) infection, which ultimately leads to liver cirrhosis, hepatic decompensation, and hepatocellular carcinoma (HCC), remains a significant disease burden worldwide. Despite the use of antiviral therapy (AVT) using oral nucleos(t)ide analogs (NUCs) with high genetic barriers, the risk of HCC development cannot be completely eliminated. Therefore, bi-annual surveillance of HCC using abdominal ultrasonography with or without tumor markers is recommended for at-risk populations. For a more precise assessment of future HCC risk at the individual level, many HCC prediction models have been proposed in the era of potent AVT with promising results. It allows prognostication according to the risk of HCC development, for example, low-vs. intermediate-vs. high-risk groups. Most of these models have the advantage of high negative predictive values for HCC development, allowing exemption from biannual HCC screening. Recently, non-invasive surrogate markers for liver fibrosis, such as vibration-controlled transient elastography, have been introduced as integral components of the equations, providing better predictive performance in general. Furthermore, beyond the conventional statistical methods that primarily depend on multi-variable Cox regression analyses based on the previous literature, newer techniques using artificial intelligence have also been applied in the design of HCC prediction models. Here, we aimed to review the HCC risk prediction models that were developed in the era of potent AVT and validated among independent cohorts to address the clinical unmet needs, as well as comment on future direction to establish the individual HCC risk more precisely.

Personalized Antiviral Drug Selection in Patients With Chronic Hepatitis B Using a Machine Learning Model: A Multinational Study

INTRODUCTION

Tenofovir disoproxil fumarate (TDF) is reportedly superior or at least comparable to entecavir (ETV) for the prevention of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B; however, it has distinct long-term renal and bone toxicities. This study aimed to develop and validate a machine learning model (designated as Prediction of Liver cancer using Artificial intelligence-driven model for Network-antiviral Selection for hepatitis B [PLAN-S]) to predict an individualized risk of HCC during ETV or TDF therapy.

METHODS

This multinational study included 13,970 patients with chronic hepatitis B. The derivation (n = 6,790), Korean validation (n = 4,543), and Hong Kong-Taiwan validation cohorts (n = 2,637) were established. Patients were classified as the TDF-superior group when a PLAN-S-predicted HCC risk under ETV treatment is greater than under TDF treatment, and the others were defined as the TDF-nonsuperior group.

RESULTS

The PLAN-S model was derived using 8 variables and generated a c-index between 0.67 and 0.78 for each cohort. The TDF-superior group included a higher proportion of male patients and patients with cirrhosis than the TDF-nonsuperior group. In the derivation, Korean validation, and Hong Kong-Taiwan validation cohorts, 65.3%, 63.5%, and 76.4% of patients were classified as the TDF-superior group, respectively. In the TDF-superior group of each cohort, TDF was associated with a significantly lower risk of HCC than ETV (hazard ratio = 0.60-0.73, all P < 0.05). In the TDF-nonsuperior group, however, there was no significant difference between the 2 drugs (hazard ratio = 1.16-1.29, all P > 0.1).

DISCUSSION

Considering the individual HCC risk predicted by PLAN-S and the potential TDF-related toxicities, TDF and ETV treatment may be recommended for the TDF-superior and TDF-nonsuperior groups, respectively.

Machine learning prediction models for clinical management of blood-borne viral infections: a systematic review of current applications and future impact

BACKGROUND

Machine learning (ML) prediction models to support clinical management of blood-borne viral infections are becoming increasingly abundant in medical literature, with a number of competing models being developed for the same outcome or target population. However, evidence on the quality of these ML prediction models are limited.

OBJECTIVE

This study aimed to evaluate the development and quality of reporting of ML prediction models that could facilitate timely clinical management of blood-borne viral infections.

METHODS

We conducted narrative evidence synthesis following the synthesis without meta-analysis guidelines. We searched PubMed and Cochrane Central Register of Controlled Trials for all studies applying ML models for predicting clinical outcomes associated with hepatitis B virus (HBV), human immunodeficiency virus (HIV), or hepatitis C virus (HCV).

RESULTS

We found 33 unique ML prediction models aiming to support clinical decision making. Overall, 12 (36.4%) focused on HBV, 10 (30.3%) on HCV, 10 on HIV (30.3%) and two (6.1%) on co-infection. Among these, six (18.2%) addressed the diagnosis of infection, 16 (48.5%) the prognosis of infection, eight (24.2%) the prediction of treatment response, two (6.1%) progression through a cascade of care, and one (3.03%) focused on the choice of antiretroviral therapy (ART). Nineteen prediction models (57.6%) were developed using data from high-income countries. Evaluation of prediction models was limited to measures of performance. Detailed information on software code accessibility was often missing. Independent validation on new datasets and/or in other institutions was rarely done.

CONCLUSION

Promising approaches for ML prediction models in blood-borne viral infections were identified, but the lack of robust validation, interpretability/explainability, and poor quality of reporting hampered their clinical relevance. Our findings highlight important considerations that can inform standard reporting guidelines for ML prediction models in the future (e.g., TRIPOD-AI), and provides critical data to inform robust evaluation of the models.

Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.

Secreted proteins encoded by super enhancer-driven genes could be promising biomarkers for early detection of esophageal squamous cell carcinoma

BACKGROUND

Early detection of cancer remains an unmet need in clinical practice, and high diagnostic sensitivity and specificity biomarkers are urgently required. Here, we attempted to identify secreted proteins encoded by super-enhancer (SE)-driven genes as diagnostic biomarkers for esophageal squamous cell carcinoma (ESCC).

METHODS

We conducted an integrative analysis of multiple data sets including ChIP-seq data, secretome data, CCLE data and GEO data to screen secreted proteins encoded by SE-driven genes. Using ELISA, we further identified up-regulated secreted proteins through a small size of clinical samples and verified in a multi-centre validation stage (345 in test cohort and 231 in validation cohort). Receiver operating characteristic curves were used to calculate diagnostic accuracy. Artificial intelligence (AI) method named gradient boosting machine (GBM) were applied for model construction to enhance diagnostic accuracy.

RESULTS

Serum EFNA1 and MMP13 were identified, and showed significantly higher levels in ESCC patients compared to normal controls. An integrated Five-Biomarker Panel (iFBPanel) established by combining EFNA1, MMP13, carcino-embryonic antigen, Cyfra21-1 and squmaous cell carcinoma antigen had AUCs of 0.881 and 0.880 for ESCC in test and validation cohorts, respectively. Importantly, the iFBPanel also exhibited good performance in detecting early-stage ESCC patients (0.872 and 0.864). Furthermore, the iFBPanel was further empowered by AI technology which showed excellent diagnostic performance in early-stage ESCC (0.927 and 0.907).

CONCLUSIONS

Our study suggested that serum EFNA1 and MMP13 could potentially assist ESCC detection, and provided an easy-to-use detection model that might help the diagnosis of early-stage ESCC.

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms

Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes. The development of artificial intelligence provides a powerful tool for accelerating the discovery of antidiabetic drugs. This work aims to establish a predictor called iPADD for discovering potential antidiabetic drugs. In the predictor, we used four kinds of molecular fingerprints and their combinations to encode the drugs and then adopted minimum-redundancy-maximum-relevance (mRMR) combined with an incremental feature selection strategy to screen optimal features. Based on the optimal feature subset, eight machine learning algorithms were applied to train models by using 5-fold cross-validation. The best model could produce an accuracy (Acc) of 0.983 with the area under the receiver operating characteristic curve (auROC) value of 0.989 on an independent test set. To further validate the performance of iPADD, we selected 65 natural products for case analysis, including 13 natural products in clinical trials as positive samples and 52 natural products as negative samples. Except for abscisic acid, our model can give correct prediction results. Molecular docking illustrated that quercetin and resveratrol stably bound with the diabetes target NR1I2. These results are consistent with the model prediction results of iPADD, indicating that the machine learning model has a strong generalization ability. The source code of iPADD is available at https://github.com/llllxw/iPADD.

A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B

BACKGROUND

Machine learning (ML) algorithms can be used to overcome the prognostic performance limitations of conventional hepatocellular carcinoma (HCC) risk models. We established and validated an ML-based HCC predictive model optimized for patients with chronic hepatitis B (CHB) infections receiving antiviral therapy (AVT).

METHODS

Treatment-naïve CHB patients who were started entecavir (ETV) or tenofovir disoproxil fumarate (TDF) were enrolled. We used a training cohort (n = 960) to develop a novel ML model that predicted HCC development within 5 years and validated the model using an independent external cohort (n = 1937). ML algorithms consider all potential interactions and do not use predefined hypotheses.

RESULTS

The mean age of the patients in the training cohort was 48 years, and most patients (68.9%) were men. During the median 59.3 (interquartile range 45.8-72.3) months of follow-up, 69 (7.2%) patients developed HCC. Our ML-based HCC risk prediction model had an area under the receiver-operating characteristic curve (AUC) of 0.900, which was better than the AUCs of CAMD (0.778) and REAL B (0.772) (both p < .05). The better performance of our model was maintained (AUC = 0.872 vs. 0.788 for CAMD and 0.801 for REAL B) in the validation cohort. Using cut-off probabilities of 0.3 and 0.5, the cumulative incidence of HCC development differed significantly among the three risk groups (p < .001).

CONCLUSIONS

Our new ML model performed better than models in terms of predicting the risk of HCC development in CHB patients receiving AVT.

Risk stratification and early detection biomarkers for precision HCC screening

Hepatocellular carcinoma (HCC) mortality remains high primarily due to late diagnosis as a consequence of failed early detection. Professional societies recommend semi-annual HCC screening in at-risk patients with chronic liver disease to increase the likelihood of curative treatment receipt and improve survival. However, recent dynamic shift of HCC etiologies from viral to metabolic liver diseases has significantly increased the potential target population for the screening, whereas annual incidence rate has become substantially lower. Thus, with the contemporary HCC etiologies, the traditional screening approach might not be practical and cost-effective. HCC screening consists of (i) definition of rational at-risk population, and subsequent (ii) repeated application of early detection tests to the population at regular intervals. The suboptimal performance of the currently available HCC screening tests highlights an urgent need for new modalities and strategies to improve early HCC detection. In this review, we overview recent developments of clinical, molecular, and imaging-based tools to address the current challenge, and discuss conceptual framework and approaches of their clinical translation and implementation. These encouraging progresses are expected to transform the current "one-size-fits-all" HCC screening into individualized precision approaches to early HCC detection and ultimately improve the poor HCC prognosis in the foreseeable future.

Inverse Propensity Score-Weighted Analysis of Entecavir and Tenofovir Disoproxil Fumarate in Patients with Chronic Hepatitis B: A Large-Scale Multicenter Study

Tenofovir disoproxil fumarate (TDF) is reportedly superior or at least comparable to entecavir (ETV) in preventing hepatocellular carcinoma (HCC) among chronic hepatitis B (CHB) patients; however, it remains controversial. This study aimed to conduct comprehensive comparisons between the two antivirals. CHB patients initially treated with ETV or TDF between 2012 and 2015 at 20 referral centers in Korea were included. The primary outcome was the cumulative incidence of HCC. The secondary outcomes included death or liver transplantation, liver-related outcome, extrahepatic malignancy, development of cirrhosis, decompensation events, complete virologic response (CVR), seroconversion rate, and safety. Baseline characteristics were balanced using the inverse probability of treatment weighting (IPTW). Overall, 4210 patients were enrolled: 1019 received ETV and 3191 received TDF. During the median follow-ups of 5.6 and 5.5 years, 86 and 232 cases of HCC were confirmed in the ETV and TDF groups, respectively. There was no difference in HCC incidence between the groups both before (p = 0.36) and after IPTW was applied (p = 0.81). Although the incidence of extrahepatic malignancy was significantly higher in the ETV group than in the TDF group before weighting (p = 0.02), no difference was confirmed after IPTW (p = 0.29). The cumulative incidence rates of death or liver transplantation, liver-related outcome, new cirrhosis development, and decompensation events were also comparable in the crude population (p = 0.24-0.91) and in the IPTW-adjusted population (p = 0.39-0.80). Both groups exhibited similar rates of CVR (ETV vs. TDF: 95.1% vs. 95.8%, p = 0.38), and negative conversion of hepatitis B e antigen (41.6% vs. 37.2%, p = 0.09) or surface antigen (2.8% vs. 1.9%, p = 0.10). Compared to the ETV group, more patients in the TDF group changed initial antivirals due to side effects, including decreased kidney function (n = 17), hypophosphatemia (n = 20), and osteoporosis (n = 18). In this large-scale multicenter study, ETV and TDF demonstrated comparable effectiveness across a broad range of outcomes in patients with treatment-naïve CHB during similar follow-up periods.

Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review

Background and Objectives: The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. The recent developments of artificial intelligence (AI) applications show a high potential for improving the accuracy of diagnosis, involving large sets of clinical data. For this reason, the aim of this systematic review is to provide a comprehensive overview of current AI applications and analyze the accuracy of these systems to perform an automated diagnosis of liver fibrosis. Materials and Methods: We searched PubMed, Cochrane Library, EMBASE, and WILEY databases using predefined keywords. Articles were screened for relevant publications about AI applications capable of diagnosing liver fibrosis. Exclusion criteria were animal studies, case reports, abstracts, letters to the editor, conference presentations, pediatric studies, studies written in languages other than English, and editorials. Results: Our search identified a total of 24 articles analyzing the automated imagistic diagnosis of liver fibrosis, out of which six studies analyze liver ultrasound images, seven studies analyze computer tomography images, five studies analyze magnetic resonance images, and six studies analyze liver biopsies. The studies included in our systematic review showed that AI-assisted non-invasive techniques performed as accurately as human experts in detecting and staging liver fibrosis. Nevertheless, the findings of these studies need to be confirmed through clinical trials to be implemented into clinical practice. Conclusions: The current systematic review provides a comprehensive analysis of the performance of AI systems in diagnosing liver fibrosis. Automatic diagnosis, staging, and risk stratification for liver fibrosis is currently possible considering the accuracy of the AI systems, which can overcome the limitations of non-invasive diagnosis methods.

Opportunities to address gaps in early detection and improve outcomes of liver cancer

Death rates from primary liver cancer (hepatocellular carcinoma [HCC]) have continued to rise in the United States over the recent decades despite the availability of an increasing range of treatment modalities, including new systemic therapies. Prognosis is strongly associated with tumor stage at diagnosis; however, most cases of HCC are diagnosed beyond an early stage. This lack of early detection has contributed to low survival rates. Professional society guidelines recommend semiannual ultrasound-based HCC screening for at-risk populations, yet HCC surveillance continues to be underused in clinical practice. On April 28, 2022, the Hepatitis B Foundation convened a workshop to discuss the most pressing challenges and barriers to early HCC detection and the need to better leverage existing and emerging tools and technologies that could improve HCC screening and early detection. In this commentary, we summarize technical, patient-level, provider-level, and system-level challenges and opportunities to improve processes and outcomes across the HCC screening continuum. We highlight promising approaches to HCC risk stratification and screening, including new biomarkers, advanced imaging incorporating artificial intelligence, and algorithms for risk stratification. Workshop participants emphasized that action to improve early detection and reduce HCC mortality is urgently needed, noting concern that many of the challenges we face today are the same or similar to those faced a decade ago and that HCC mortality rates have not meaningfully improved. Increasing the uptake of HCC screening was identified as a short-term priority while developing and validating better screening tests and risk-appropriate surveillance strategies.

The role of artificial intelligence in hepatology research and practice

PURPOSE OF REVIEW

The use of artificial intelligence (AI) in examining large data sets has recently gained considerable attention to evaluate disease epidemiology, management approaches, and disease outcomes. The purpose of this review is to summarize the current role of AI in contemporary hepatology practice.

RECENT FINDINGS

AI was found to be diagnostically valuable in the evaluation of liver fibrosis, detection of cirrhosis, differentiation between compensated and decompensated cirrhosis, evaluation of portal hypertension, detection and differentiation of particular liver masses, preoperative evaluation of hepatocellular carcinoma as well as response to treatment and estimation of graft survival in patients undergoing liver transplantation. AI additionally holds great promise in examination of structured electronic health records data as well as in examination of clinical text (using various natural language processing approaches). Despite its contributions, AI has several limitations, including the quality of existing data, small cohorts with possible sampling bias and the lack of well validated easily reproducible models.

SUMMARY

AI and deep learning models have extensive applicability in assessing liver disease. However, multicenter randomized controlled trials are indispensable to validate their utility.

Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.

A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients

OBJECTIVE

Estimating the deterioration paths of chronic hepatitis B (CHB) patients is critical for physicians' decisions and patient management. A novel, hierarchical multilabel graph attention-based method aims to predict patient deterioration paths more effectively. Applied to a CHB patient data set, it offers strong predictive utilities and clinical value.

MATERIALS AND METHODS

The proposed method incorporates patients' responses to medications, diagnosis event sequences, and outcome dependencies to estimate deterioration paths. From the electronic health records maintained by a major healthcare organization in Taiwan, we collect clinical data about 177 959 patients diagnosed with hepatitis B virus infection. We use this sample to evaluate the proposed method's predictive efficacy relative to 9 existing methods, as measured by precision, recall, F-measure, and area under the curve (AUC).

RESULTS

We use 20% of the sample as holdouts to test each method's prediction performance. The results indicate that our method consistently and significantly outperforms all benchmark methods. It attains the highest AUC, with a 4.8% improvement over the best-performing benchmark, as well as 20.9% and 11.4% improvements in precision and F-measures, respectively. The comparative results demonstrate that our method is more effective for predicting CHB patients' deterioration paths than existing predictive methods.

DISCUSSION AND CONCLUSION

The proposed method underscores the value of patient-medication interactions, temporal sequential patterns of distinct diagnosis, and patient outcome dependencies for capturing dynamics that underpin patient deterioration over time. Its efficacious estimates grant physicians a more holistic view of patient progressions and can enhance their clinical decision-making and patient management.

Data-Driven Assisted Decision Making for Surgical Procedure of Hepatocellular Carcinoma Resection and Prognostic Prediction: Development and Validation of Machine Learning Models

Background: Currently, surgical decisions for hepatocellular carcinoma (HCC) resection are difficult and not sufficiently personalized. We aimed to develop and validate data driven prediction models to assist surgeons in selecting the optimal surgical procedure for patients. Methods: Retrospective data from 361 HCC patients who underwent radical resection in two institutions were included. End-to-end deep learning models were built to automatically segment lesions from the arterial phase (AP) of preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Clinical baseline characteristics and radiomic features were rigorously screened. The effectiveness of radiomic features and radiomic-clinical features was also compared. Three ensemble learning models were proposed to perform the surgical procedure decision and the overall survival (OS) and recurrence-free survival (RFS) predictions after taking different solutions, respectively. Results: SegFormer performed best in terms of automatic segmentation, achieving a Mean Intersection over Union (mIoU) of 0.8860. The five-fold cross-validation results showed that inputting radiomic-clinical features outperformed using only radiomic features. The proposed models all outperformed the other mainstream ensemble models. On the external test set, the area under the receiver operating characteristic curve (AUC) of the proposed decision model was 0.7731, and the performance of the prognostic prediction models was also relatively excellent. The application web server based on automatic lesion segmentation was deployed and is available online. Conclusions: In this study, we developed and externally validated the surgical decision-making procedures and prognostic prediction models for HCC for the first time, and the results demonstrated relatively accurate predictions and strong generalizations, which are expected to help clinicians optimize surgical procedures.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Importance

Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.

Highlights

We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.

Conclusion

AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.

Comparable outcomes between immune-tolerant and active phases in noncirrhotic chronic hepatitis B: a meta-analysis

BACKGROUND

Antiviral therapy is not indicated for patients with chronic hepatitis B (CHB) in the immune-tolerant (IT) phase. We compared the outcomes between the untreated IT phase and the treated immune-active (IA) phase in noncirrhotic HBeAg-positive CHB patients.

METHODS

We systematically searched 4 databases, including PubMed, Medline, Embase, and Cochrane, until August 2021. The pooled incidence rates of HCC and mortality in the IT and IA cohorts and phase change in the IT cohort were investigated. Studies that included patients with liver cirrhosis were excluded.

RESULTS

Thirteen studies involving 11,903 patients were included. The overall median of the median follow-up period was 62.4 months. The pooled 5-year and 10-year incidence rates of HCC were statistically similar between the IT and IA cohorts (1.1%, 95% CI: 0.4%-2.8% vs. 1.1%, 95% CI: 0.5%-2.3%, and 2.7%, 95% CI: 1.0%-7.3% vs. 3.6%, 95% CI: 2.4%-5.5%, respectively, all p>0.05). The pooled 5-year odds ratio of HCC between IT and IA cohorts was 1.05 (95% CI: 0.32-3.45; p=0.941). The pooled 5-year incidence rate of mortality was statistically similar between the IT and IA cohorts (1.9%, 95% CI: 1.1%-3.4% vs. 1.0%, 95% CI: 0.3%-2.9%, p=0.285). Finally, the pooled 5-year incidence rate of phase change in the IT cohort was 36.1% (95% CI: 29.5%-43.2%).

CONCLUSION

The pooled incidence rates of HCC and mortality were comparable between the untreated IT and the treated IA phases in noncirrhotic HBeAg-positive CHB patients.

Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.

Exploration of the intelligent-auxiliary design of architectural space using artificial intelligence model

In order to carry out a comprehensive design description of the specific architectural model of AI, the auxiliary model of AI and architectural spatial intelligence is deeply integrated, and flexible design is carried out according to the actual situation. AI assists in the generation of architectural intention and architectural form, mainly supporting academic and working theoretical models, promoting technological innovation, and thus improving the design efficiency of the architectural design industry. AI-aided architectural design enables every designer to achieve design freedom. At the same time, with the help of AI, architectural design can complete the corresponding work faster and more efficiently. With the help of AI technology, through the adjustment and optimization of keywords, AI automatically generates a batch of architectural space design schemes. Against this background, the auxiliary model of architectural space design is established through the literature research of the AI model, the architectural space intelligent auxiliary model, and the semantic network and the internal structure analysis of architectural space. Secondly, to ensure compliance with the three-dimensional characteristics of the architectural space from the data source, based on the analysis of the overall function and structure of space design, the intelligent design of the architectural space auxiliary by Deep Learning is carried out. Finally, it takes the 3D model selected in the UrbanScene3D data set as the research object, and the auxiliary performance of AI's architectural space intelligent model is tested. The research results show that with the increasing number of network nodes, the model fitting degree on the test data set and training data set is decreasing. The fitting curve of the comprehensive model shows that the intelligent design scheme of architectural space based on AI is superior to the traditional architectural design scheme. As the number of nodes in the network connection layer increases, the intelligent score of space temperature and humidity will continue to rise. The model can achieve the optimal intelligent auxiliary effect of architectural space. The research has practical application value for promoting the intelligent and digital transformation of architectural space design.

The potential role of machine learning in modelling advanced chronic liver disease

The use of artificial intelligence is rapidly increasing in medicine to support clinical decision making mostly through diagnostic and prediction models. Such models derive from huge databases (big data) including a large variety of health-related individual patient data (input) and the corresponding diagnosis and/or outcome (labels). Various types of algorithms (e.g. neural networks) based on powerful computational ability (machine), allow to detect the relationship between input and labels (learning). More complex algorithms, like recurrent neural network can learn from previous as well as actual input (deep learning) and are used for more complex tasks like imaging analysis and personalized (bespoke) medicine. The prompt availability of big data makes that artificial intelligence can provide rapid answers to questions that would require years of traditional clinical research. It may therefore be a key tool to overcome several major gaps in the model of advanced chronic liver disease, mostly transition from mild to clinically significant portal hypertension, the impact of acute decompensation and the role of further decompensation and treatment efficiency. However, several limitations of artificial intelligence should be overcome before its application in clinical practice. Assessment of the risk of bias, understandability of the black boxes developing the models and models' validation are the most important areas deserving clarification for artificial intelligence to be widely accepted from physicians and patients.

Suboptimal Performance of Hepatocellular Carcinoma Prediction Models in Patients with Hepatitis B Virus-Related Cirrhosis

This study aimed to evaluate the predictive performance of pre-existing well-validated hepatocellular carcinoma (HCC) prediction models, established in patients with HBV-related cirrhosis who started potent antiviral therapy (AVT). We retrospectively reviewed the cases of 1339 treatment-naïve patients with HBV-related cirrhosis who started AVT (median period, 56.8 months). The scores of the pre-existing HCC risk prediction models were calculated at the time of AVT initiation. HCC developed in 211 patients (15.1%), and the cumulative probability of HCC development at 5 years was 14.6%. Multivariate Cox regression analysis revealed that older age (adjusted hazard ratio [aHR], 1.023), lower platelet count (aHR, 0.997), lower serum albumin level (aHR, 0.578), and greater LS value (aHR, 1.012) were associated with HCC development. Harrell’s c-indices of the PAGE-B, modified PAGE-B, modified REACH-B, CAMD, aMAP, HCC-RESCUE, AASL-HCC, Toronto HCC Risk Index, PLAN-B, APA-B, CAGE-B, and SAGE-B models were suboptimal in patients with HBV-related cirrhosis, ranging from 0.565 to 0.667. Nevertheless, almost all patients were well stratified into low-, intermediate-, or high-risk groups according to each model (all log-rank p < 0.05), except for HCC-RESCUE (p = 0.080). Since all low-risk patients had cirrhosis at baseline, they had unneglectable cumulative incidence of HCC development (5-year incidence, 4.9−7.5%). Pre-existing risk prediction models for patients with chronic hepatitis B showed suboptimal predictive performances for the assessment of HCC development in patients with HBV-related cirrhosis.

What to do about hepatocellular carcinoma: Recommendations for health authorities from the International Liver Cancer Association

Hepatocellular carcinoma (HCC) is a major public health problem worldwide for which the incidence and mortality are similar, pointing to the lack of effective treatment options. Knowing the different issues involved in the management of HCC, from risk factors to screening and management, is essential to improve the prognosis and quality of life of affected individuals. This document summarises the current state of knowledge and the unmet needs for all the different stakeholders in the care of liver cancer, meaning patients, relatives, physicians, regulatory agencies and health authorities so that optimal care can be delivered to patients. The document was commissioned by the International Liver Cancer Association and was reviewed by senior members, including two ex-presidents of the Association. This document lays out the recommended approaches to the societal management of HCC based on the economic status of a given region.

A Mac-2 Binding Protein Glycosylation Isomer-Based Risk Model Predicts Hepatocellular Carcinoma in HBV-Related Cirrhotic Patients on Antiviral Therapy

Mac-2 binding protein glycosylation isomer (M2BPGi) has not been used in a risk score to predict hepatocellular carcinoma (HCC). We enrolled 1003 patients with chronic hepatitis B and cirrhosis receiving entecavir or tenofovir therapy for more than12 months to construct an HCC risk score. In the development cohort, Cox regression analysis identified male gender, age, platelet count, AFP and M2BPGi levels at 12 months of treatment as independent risk factors of HCC. We developed the HCC risk prediction model, the ASPAM-B score, based on age, sex, platelet count, AFP and M2BPGi levels at 12 months of treatment, with the total scores ranging from 0 to 11.5. This risk model accurately classified patients into low (0−3.5), medium (4−7), and high (>7) risk in the development and validation groups (p < 0.001). The areas under the receiver operating characteristic curve (AUROC) of 3-, 5- and 9-year risks of HCC were 0.742, 0.728 and 0.719, respectively, in the development cohort. All AUROC between the ASPAM-B and APA-B, PAGE-B, RWS-HCC and THRI scores at 3−9 years were significantly different. The M2BPGi-based risk model exhibited good discriminant function in predicting HCC in cirrhotic patients who received long-term antiviral treatment.

Hepatocellular carcinoma

Hepatocellular carcinoma is one of the most common cancers worldwide and represents a major global health-care challenge. Although viral hepatitis and alcohol remain important risk factors, non-alcoholic fatty liver disease is rapidly becoming a dominant cause of hepatocellular carcinoma. A broad range of treatment options are available for patients with hepatocellular carcinoma, including liver transplantation, surgical resection, percutaneous ablation, and radiation, as well as transarterial and systemic therapies. As such, clinical decision making requires a multidisciplinary team that longitudinally adapts the individual treatment strategy according to the patient's tumour stage, liver function, and performance status. With the approval of new first-line agents and second-line agents, as well as the establishment of immune checkpoint inhibitor-based therapies as standard of care, the treatment landscape of advanced hepatocellular carcinoma is more diversified than ever. Consequently, the outlook for patients with hepatocellular carcinoma has improved. However, the optimal sequencing of drugs remains to be defined, and predictive biomarkers are urgently needed to inform treatment selection. In this Seminar, we present an update on the causes, diagnosis, molecular classification, and treatment of hepatocellular carcinoma.

Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research

In this paper, in-depth research analysis of anti-hepatocellular carcinoma molecular targets for hepatocellular carcinoma diagnosis was conducted using artificial intelligence. Because BRD4 plays an important role in gene transcription for cell cycle regulation and apoptosis, tumor-targeted therapy by inhibiting the expression or function of BRD4 has received increasing attention in the field of antitumor research. Study subjects in small samples were used as the validation set for validating each diagnostic model constructed based on the training set. The diagnostic effect of each model in the validation set is evaluated by calculating the sensitivity, specificity, and compliance rate, and the model with the best and most stable diagnostic value is selected by combining the results of model construction, validation, and evaluation. The total sample was divided into a training set and test set by using a stratified sampling method in the ratio of 7 : 3. Logistic regression, weighted k-nearest neighbor, decision tree, and BP artificial neural network were used in the training set to construct diagnostic models for early-stage liver cancer, respectively, and the optimal parameters of the corresponding models were obtained, and then, the constructed models were validated in the test set. To evaluate the diagnostic efficacy, stability, and generalization ability of the four classification methods more robustly, a 10-fold crossover test was performed for each classification method. BRD4 is an epigenetic regulator that is associated with the upregulation of expression of various oncogenic drivers in tumors. Targeting BRD4 with pharmacological inhibitors has emerged as a novel approach for tumor treatment. However, before we implemented this topic, there were no detailed studies on whether BRD4 could be used for the treatment of HCC, the role of BRD4 in HCC cell proliferation and apoptosis, and the ability of small molecule BRD4 inhibitors to induce apoptosis in hepatocellular carcinoma cells.

Precision medicine in the era of potent antiviral therapy for chronic hepatitis B

With the wide use of potent and safe nucloes(t-)ide analogues (NAs) treatment, patient-centered care is getting important. Intensive care for comorbidity has gain utmost importance in care of aging chronic hepatitis B (CHB) patients with life-long antiviral treatment. Linkage to care of patients with CHB is essential for the goal of hepatitis B virus (HBV) eradication. As long-term suppression of HBV DNA replication does not prevent hepatocellular carcinoma (HCC), prevention of HCC is another challenge for NAs treatment. There is a possibility of hepatocarcinogenesis in the immune-tolerant phase and risk of loss of patients during active monitoring seeking the time point for antiviral treatment initiation. Initiation of NAs treatment from the immune-tolerant phase would improve the linkage to care. However, universal recommendation is premature and evidence for cost-effectiveness needs to be accumulated. Early initiation of NAs in the evidence of significant disease progression, either HBV associated or comorbidity associated, would be a better strategy to reduce the risk of HCC in patients located in the gray zone.

Development and Internal Validation of a Nomogram to Predict Mortality During the ICU Stay of Thoracic Fracture Patients Without Neurological Compromise: An Analysis of the MIMIC-III Clinical Database

Background: This study aimed to develop and validate a nomogram for predicting mortality in patients with thoracic fractures without neurological compromise and hospitalized in the intensive care unit. Methods: A total of 298 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included in the study, and 35 clinical indicators were collected within 24 h of patient admission. Risk factors were identified using the least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was established, and a nomogram was constructed. Internal validation was performed by the 1,000 bootstrap samples; a receiver operating curve (ROC) was plotted, and the area under the curve (AUC), sensitivity, and specificity were calculated. In addition, the calibration of our model was evaluated by the calibration curve and Hosmer-Lemeshow goodness-of-fit test (HL test). A decision curve analysis (DCA) was performed, and the nomogram was compared with scoring systems commonly used during clinical practice to assess the net clinical benefit. Results: Indicators included in the nomogram were age, OASIS score, SAPS II score, respiratory rate, partial thromboplastin time (PTT), cardiac arrhythmias, and fluid-electrolyte disorders. The results showed that our model yielded satisfied diagnostic performance with an AUC value of 0.902 and 0.883 using the training set and on internal validation. The calibration curve and the Hosmer-Lemeshow goodness-of-fit (HL). The HL tests exhibited satisfactory concordance between predicted and actual outcomes (P = 0.648). The DCA showed a superior net clinical benefit of our model over previously reported scoring systems. Conclusion: In summary, we explored the incidence of mortality during the ICU stay of thoracic fracture patients without neurological compromise and developed a prediction model that facilitates clinical decision making. However, external validation will be needed in the future.

Development and validation of a risk prediction model for incident liver cancer

Objective

We aimed to develop and validate a risk prediction model for liver cancer based on routinely available risk factors using the data from UK Biobank prospective cohort study.

Methods

This analysis included 359,489 participants (2,894,807 person-years) without a previous diagnosis of cancer. We used the Fine-Gray regression model to predict the incident risk of liver cancer, accounting for the competing risk of all-cause death. Model discrimination and calibration were validated internally. Decision curve analysis was conducted to quantify the clinical utility of the model. Nomogram was built based on regression coefficients.

Results

Good discrimination performance of the model was observed in both development and validation datasets, with an area under the curve (95% confidence interval) for 5-year risk of 0.782 (0.748-0.816) and 0.771 (0.702-0.840) respectively. The calibration showed fine agreement between observed and predicted risks. The model yielded higher positive net benefits in the decision curve analysis than considering either all participants as being at high or low risk, which indicated good clinical utility.

Conclusion

A new risk prediction model for liver cancer composed of routinely available risk factors was developed. The model had good discrimination, calibration and clinical utility, which may help with the screening and management of liver cancer for general population in the public health field.

The best predictive model for hepatocellular carcinoma in patients with chronic hepatitis B infection

Chronic hepatitis B (CHB) seriously threatens human health. About 820,000 deaths annually are due to related complications such as hepatitis B and hepatocellular carcinoma (HCC). Recently, the use of oral antiviral agents has significantly improved the prognosis of patients with CHB infection and reduced the risk of HCC. However, hepatitis B virus still remains a major factor in the development of HCC, raising many concerns. Therefore, numerous studies have been conducted to assess the risk of HCC in patients with CHB infection and many models have been proposed to predict the risk of developing HCC. However, as each study has different models for predicting HCC development that can be applied depending on the use of antiviral agents or the type of antiviral agents, it is necessary to properly understand characteristics of each model when using it for the evaluation of HCC in patients with CHB infection. In addition, because different variables such as host factor, viral activity, and cirrhosis are used to evaluate the risk of HCC development, it is necessary to assess the risk by carefully verifying which variables are used. Recently, studies have also evaluated the risk of HCC using risk prediction models through transient elastography and artificial intelligence (AI) system. These HCC risk predication models are also noteworthy. In this review, we aimed to compare HCC risk prediction models in patients with CHB infection reported to date to confirm variables used and specificity between each model to determine an appropriate HCC risk prediction method.